{
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.4-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "python37464bitbasecondaf8f36033e94b4df5b3beb8b16f94aed3",
   "display_name": "Python 3.7.4 64-bit ('base': conda)"
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from PIL import Image\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from process import Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_path_left = './data/images/im0.png'\n",
    "image_path_right = './data/images/im1.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Image size: (357, 250)\nImage mode: L\nImage size: (357, 250)\nImage mode: L\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "((357, 250), 'L')"
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "imL = Process(image_path_left)\n",
    "imL.resize_image(357, 250)\n",
    "arrL = imL.get_array()\n",
    "imL.get_info()\n",
    "\n",
    "imR = Process(image_path_right)\n",
    "imR.resize_image(357, 250)\n",
    "arrR = imR.get_array()\n",
    "imR.get_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import ndimage\n",
    "\n",
    "# activate for laplacian filter\n",
    "# arrL_filtered = ndimage.laplace(arrL)\n",
    "# arrR_filtered = ndimage.laplace(arrR)\n",
    "\n",
    "array_left = arrL\n",
    "array_right = arrR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0 rows completed.\n10 rows completed.\n20 rows completed.\n30 rows completed.\n40 rows completed.\n50 rows completed.\n60 rows completed.\n70 rows completed.\n80 rows completed.\n90 rows completed.\n100 rows completed.\n110 rows completed.\n120 rows completed.\n130 rows completed.\n140 rows completed.\n150 rows completed.\n160 rows completed.\n170 rows completed.\n180 rows completed.\n190 rows completed.\n200 rows completed.\n210 rows completed.\n220 rows completed.\n230 rows completed.\nTotal runtime: 30.129307985305786\n(239, 346)\n"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Calculate Disparity using Sum of Absolute Differences Method (SAD)\n",
    "__________________________________________________________________\n",
    "\n",
    "Region matching between windows in the left and right images using the SAD method. \n",
    "Modify 'size' and 'search_range' to alter results.\n",
    "\"\"\"\n",
    "\n",
    "size = 11\n",
    "\n",
    "search_range = 44\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "disp_matrix = []\n",
    "\n",
    "for row in range(len(arrL_filtered) - size):\n",
    "\n",
    "    if row % 10 == 0:\n",
    "        print(f\"{row} rows completed.\")\n",
    "\n",
    "    disps = []\n",
    "\n",
    "    for col1 in range(len(arrL_filtered[row]) - size):\n",
    "        win1 = arrL_filtered[row:row + size, col1:col1 + size].flatten()\n",
    "\n",
    "        if col1 < search_range:\n",
    "            init = 0\n",
    "        else:\n",
    "            init = col1 - search_range\n",
    "\n",
    "        sads = []\n",
    "\n",
    "        for col2 in range(col1, init - 1, -1):\n",
    "            win2 = arrR_filtered[row:row + size, col2:col2 + size].flatten()\n",
    "\n",
    "            sad = np.sum(np.abs(np.subtract(win1, win2)))\n",
    "            sads.append(sad)\n",
    "\n",
    "        disparity = np.argmin(sads)\n",
    "        disps.append(disparity)\n",
    "\n",
    "    disp_matrix.append(disps)\n",
    "            \n",
    "        \n",
    "disp_matrix = np.array(disp_matrix)\n",
    "\n",
    "end_time = time.time()\n",
    "\n",
    "print(f\"Total runtime: {end_time - start_time}\")\n",
    "print(disp_matrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0 rows completed.\n10 rows completed.\n20 rows completed.\n30 rows completed.\n40 rows completed.\n50 rows completed.\n60 rows completed.\n70 rows completed.\n80 rows completed.\n90 rows completed.\n100 rows completed.\n110 rows completed.\n120 rows completed.\n130 rows completed.\n140 rows completed.\n150 rows completed.\n160 rows completed.\n170 rows completed.\n180 rows completed.\n190 rows completed.\n200 rows completed.\n210 rows completed.\n220 rows completed.\n230 rows completed.\nTotal runtime: 171.39865589141846\n(239, 346)\n"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Calculate Disparity using Correlation Coefficient Method (CC)\n",
    "__________________________________________________________________\n",
    "\n",
    "Region matching between windows in the left and right images using the CC method. \n",
    "Modify 'size' and 'search_range' to alter results.\n",
    "\"\"\"\n",
    "window_size = 11\n",
    "\n",
    "search_range = 44\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "disp_matrix = []\n",
    "\n",
    "for row in range(len(arrL_filtered) - size):\n",
    "\n",
    "    if row % 10 == 0:\n",
    "        print(f\"{row} rows completed.\")\n",
    "\n",
    "    disps = []\n",
    " \n",
    "    for col1 in range(len(array_left[row]) - window_size):\n",
    "        win1 = array_left[row:row + window_size, col1:col1 + window_size].flatten()\n",
    "        avg1 = np.average(win1)\n",
    "        var1 = np.subtract(win1, avg1)\n",
    "\n",
    "        if col1 < search_range:\n",
    "            init = 0\n",
    "        else:\n",
    "            init = col1 - search_range\n",
    "\n",
    "        if col1 > (len(array_left[row]) - window_size - search_range):\n",
    "            final = len(array_left[row]) - window_size\n",
    "        else:\n",
    "            final = col1 + search_range\n",
    "\n",
    "        corrs = []\n",
    "        for col2 in range(col1, init - 1, -1):\n",
    "            win2 = array_right[row:row + window_size, col2:col2 + window_size].flatten()\n",
    "\n",
    "            avg2 = np.average(win2)\n",
    "            var2 = np.subtract(win2, avg2)\n",
    "\n",
    "            if (np.sqrt((np.sum(np.square(var1))) * (np.sum(np.square(var2))))) == 0:\n",
    "                r = 0.9999999999999999\n",
    "\n",
    "            else:\n",
    "                r = (np.sum(np.multiply(var1, var2))) / np.sqrt((np.sum(np.square(var1))) * (np.sum(np.square(var2))))\n",
    "\n",
    "            corrs.append(r)\n",
    "\n",
    "        disparity = np.argmax(corrs)\n",
    "        disps.append(disparity)\n",
    "\n",
    "    disp_matrix.append(disps)\n",
    "            \n",
    "        \n",
    "disp_matrix = np.array(disp_matrix)\n",
    "\n",
    "end_time = time.time()\n",
    "\n",
    "print(f\"Total runtime: {end_time - start_time}\")\n",
    "print(disp_matrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[ 0,  0,  0, ..., 15, 15, 15],\n       [ 0,  0,  0, ..., 15, 15, 14],\n       [ 0,  0,  0, ..., 14, 14, 14],\n       ...,\n       [ 0,  0,  2, ..., 14, 14, 14],\n       [ 0,  0,  2, ..., 15, 15, 15],\n       [ 0,  0,  1, ..., 15, 15, 15]])"
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "\"\"\"\n",
    "Recycle Bin Image Calibration\n",
    "_____________________________\n",
    "\n",
    "Calculate the z-coordinate for each pixel using constants in 'calib.txt'\n",
    "\"\"\"\n",
    "\n",
    "z_matrix = np.copy(disp_matrix)\n",
    "\n",
    "for i in np.nditer(z_matrix):\n",
    "    i = 178.232 * 2945.377 / (i + 170.681)\n",
    "\n",
    "z_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x121ddbbd0>"
     },
     "metadata": {},
     "execution_count": 32
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"251.862448pt\" version=\"1.1\" viewBox=\"0 0 355.275115 251.862448\" width=\"355.275115pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M -0 251.862448 \nL 355.275115 251.862448 \nL 355.275115 0 \nL -0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 227.984323 \nL 348.075115 227.984323 \nL 348.075115 10.544323 \nL 33.2875 10.544323 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p7a1980c5e8)\">\n    <image height=\"218\" 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.imshow(disp_matrix)\n",
    "# plt.savefig('./data/output/MPL.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Optional: Save matrix in a .pkl file for future usage.\n",
    "\"\"\"\n",
    "\n",
    "import pickle\n",
    "\n",
    "with open('./data/output/z.pkl', 'wb') as f:\n",
    "    pickle.dump(z_matrix, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x11f6e9bd0>"
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\"\"\"\n",
    "Post-processing\n",
    "_______________\n",
    "\n",
    "Take the output matrix from disparity calculations and handle noise and discontinuities.\n",
    "\n",
    "Noise:\n",
    "\n",
    "    - Mode Method: Calculate the mode value in a large window around each pixel; assign to value if exceeds threshold.\n",
    "\n",
    "    - Threshold Cutoff Method: Flatten any extraneous pixels above a threshold to a preset value.\n",
    "\n",
    "Discontinuity:\n",
    "\n",
    "    - Average Method: Calculate the average in small window around each pixel; assign value if difference between disparity\n",
    "                      and value exceed a threshold.\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "from process import Process\n",
    "\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "raw_img = './data/images/im0.png'\n",
    "\n",
    "img = Image.open(raw_img)\n",
    "img = img.resize((disp_matrix.shape[1], disp_matrix.shape[0]))\n",
    "arr = np.array(img)\n",
    "\n",
    "xyzrgb = []\n",
    "\n",
    "avg_disp = np.copy(disp_matrix)\n",
    "\n",
    "for x in range(disp_matrix.shape[1]):\n",
    "    for y in range(disp_matrix.shape[0]):\n",
    "\n",
    "        # MEAN\n",
    "        avg = np.mean(avg_disp[y-7:y+8, x-7:x+8])\n",
    "        if avg_disp[y, x] - avg > 5:\n",
    "            avg_disp[y, x] = avg\n",
    "\n",
    "        # MODE\n",
    "        if x > 12 and x < 227:\n",
    "            if avg_disp[y, x] > 25:\n",
    "                mode = stats.mode(avg_disp[y-12:y+13, x-12:x+13].flatten())\n",
    "                avg_disp[y, x] = mode[0][0]\n",
    "\n",
    "        # THRESHOLD\n",
    "        if avg_disp[y, x] > 30:\n",
    "            avg_disp[y, x] = 25\n",
    "\n",
    "        z = np.multiply(avg_disp[y, x], 6)\n",
    "        rgb = arr[y, x]\n",
    "        xyzrgb.append([x, y, z, rgb[0], rgb[1], rgb[2]])\n",
    "\n",
    "df = pd.DataFrame(xyzrgb)\n",
    "df.columns = ['x', 'y', 'z', 'r', 'g', 'b']\n",
    "df.to_csv('./data/output/point_cloud.txt', index=False)\n",
    "plt.imshow(avg_disp)\n",
    "plt.savefig('./data/output/disp_MPL.png')"
   ]
  }
 ]
}